Triple
T31041632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ariel DuBois |
E791009
|
entity |
| Predicate | hasPlotTheme |
P76865
|
FINISHED |
| Object | balancing normal teen life with psychic experiences |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: balancing normal teen life with psychic experiences | Statement: [Ariel DuBois, hasPlotTheme, balancing normal teen life with psychic experiences]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPlotTheme Context triple: [Ariel DuBois, hasPlotTheme, balancing normal teen life with psychic experiences]
-
A.
hasPlot
Indicates that an entity (such as a narrative work) possesses or is associated with a specific storyline or sequence of events.
-
B.
hasThemeType
Indicates that something is associated with or characterized by a particular thematic category or type.
-
C.
hasThemeInStory
chosen
Indicates that a particular theme is present or plays a significant role within a given story.
-
D.
hasThemeRelationship
Indicates a relationship where one entity is thematically related to, or centered around, another entity as its main subject or topic.
-
E.
hasMotiveTheme
Indicates that an action, event, or situation is associated with a central motivating theme or underlying driving idea.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f224ca2fa881908a3ac5fedf207b90 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: April 29, 2026, 8:59 p.m.